SHAP has three properties: local accuracy, missingness, and consistency [1]}. Local accuracy means that when approximating the original model for a specific input \(x\) , local accuracy requires the explanation model to at least match the output of the model for the simplified input \(x^{\prime }\) . Missingness means that if there is a feature missing in the sample, it does not affect the output of the model. Consistency means that when the model changes and the marginal contribution of a feature increases, the corresponding Shapley value will also increase. Therefore, it is more accurate and scientific in interpreting machine learning models due to those three characteristics.
